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Classifier calibration

Confidence calibration – the problem of predicting probability estimates representative of the true correctness likelihood – is important for classification models in many applications. The two common calibration metrics are Expected Calibration Error (ECE) and Maximum Calibration Error (MCE).

Papers

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Showing 1–25 of 29 papers

TitleStatusHype
No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed ClassifierCode1
Masksembles for Uncertainty EstimationCode1
Multi-class probabilistic classification using inductive and cross Venn-Abers predictorsCode1
FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous DataCode1
PrePrompt: Predictive prompting for class incremental learningCode1
Generalized and Incremental Few-Shot Learning by Explicit Learning and Calibration without ForgettingCode1
Multivariate Confidence Calibration for Object DetectionCode1
Danish Fungi 2020 -- Not Just Another Image Recognition DatasetCode1
How Well Do Self-Supervised Models Transfer?Code1
Hidden Heterogeneity: When to Choose Similarity-Based CalibrationCode0
Accuracy-Preserving Calibration via Statistical Modeling on Probability SimplexCode0
Long-tailed Medical Diagnosis with Relation-aware Representation Learning and Iterative Classifier CalibrationCode0
No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID DataCode0
Packed-Ensembles for Efficient Uncertainty EstimationCode0
Improved User Identification through Calibrated Monte-Carlo DropoutCode0
Classifier Calibration: with application to threat scores in cybersecurityCode0
Enhancing Generalized Few-Shot Semantic Segmentation via Effective Knowledge TransferCode0
Expeditious Saliency-guided Mix-up through Random Gradient ThresholdingCode0
Class-wise and reduced calibration methodsCode0
What is Your Metric Telling You? Evaluating Classifier Calibration under Context-Specific Definitions of Reliability—0
Better Classifier Calibration for Small Data Sets—0
Binary Classifier Calibration: Bayesian Non-Parametric Approach—0
Binary Classifier Calibration: Non-parametric approach—0
Binary Classifier Calibration using an Ensemble of Near Isotonic Regression Models—0
Classifier Calibration: A survey on how to assess and improve predicted class probabilities—0
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